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Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks.

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data2,5,6. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors7,8. Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects5,6. Cross-validation within the derivation set can be performed using bootstrap resampling techniques9. Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets10. Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation11. These standardized values can in turn be used to assist in differential diagnosis12,13 and to assess disease progression and treatment effects at the network level7,14-16. We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.

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A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Authors: Bianca DeBenedictis, J. Bruce Morton.

Institutions: University of Western Ontario.

The ability to adjust behavior to sudden changes in the environment develops gradually in childhood and adolescence. For example, in the Dimensional Change Card Sort task, participants switch from sorting cards one way, such as shape, to sorting them a different way, such as color. Adjusting behavior in this way exacts a small performance cost, or switch cost, such that responses are typically slower and more error-prone on switch trials in which the sorting rule changes as compared to repeat trials in which the sorting rule remains the same. The ability to flexibly adjust behavior is often said to develop gradually, in part because behavioral costs such as switch costs typically decrease with increasing age. Why aspects of higher-order cognition, such as behavioral flexibility, develop so gradually remains an open question. One hypothesis is that these changes occur in association with functional changes in broad-scale cognitive control networks. On this view, complex mental operations, such as switching, involve rapid interactions between several distributed brain regions, including those that update and maintain task rules, re-orient attention, and select behaviors. With development, functional connections between these regions strengthen, leading to faster and more efficient switching operations. The current video describes a method of testing this hypothesis through the collection and multivariate analysis of fMRI data from participants of different ages.

Functional connectivity MRI (fcMRI) is an fMRI method that examines the connectivity of different brain areas based on the correlation of BOLD signal fluctuations over time. Temporal Lobe Epilepsy (TLE) is the most common type of adult epilepsy and involves multiple brain networks. The default mode network (DMN) is involved in conscious, resting state cognition and is thought to be affected in TLE where seizures cause impairment of consciousness. The DMN in epilepsy was examined using seed based fcMRI. The anterior and posterior hubs of the DMN were used as seeds in this analysis. The results show a disconnection between the anterior and posterior hubs of the DMN in TLE during the basal state. In addition, increased DMN connectivity to other brain regions in left TLE along with decreased connectivity in right TLE is revealed. The analysis demonstrates how seed-based fcMRI can be used to probe cerebral networks in brain disorders such as TLE.

The patch-clamp technique is today the most well-established method for recording electrical activity from individual neurons or their subcellular compartments. Nevertheless, achieving stable recordings, even from individual cells, remains a time-consuming procedure of considerable complexity. Automation of many steps in conjunction with efficient information display can greatly assist experimentalists in performing a larger number of recordings with greater reliability and in less time. In order to achieve large-scale recordings we concluded the most efficient approach is not to fully automatize the process but to simplify the experimental steps and reduce the chances of human error while efficiently incorporating the experimenter's experience and visual feedback. With these goals in mind we developed a computer-assisted system which centralizes all the controls necessary for a multi-electrode patch-clamp experiment in a single interface, a commercially available wireless gamepad, while displaying experiment related information and guidance cues on the computer screen. Here we describe the different components of the system which allowed us to reduce the time required for achieving the recording configuration and substantially increase the chances of successfully recording large numbers of neurons simultaneously.

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Authors: Marc N. Coutanche, Sharon L. Thompson-Schill.

Institutions: University of Pennsylvania.

It is now appreciated that condition-relevant information can be present within distributed patterns of functional magnetic resonance imaging (fMRI) brain activity, even for conditions with similar levels of univariate activation. Multi-voxel pattern (MVP) analysis has been used to decode this information with great success. FMRI investigators also often seek to understand how brain regions interact in interconnected networks, and use functional connectivity (FC) to identify regions that have correlated responses over time. Just as univariate analyses can be insensitive to information in MVPs, FC may not fully characterize the brain networks that process conditions with characteristic MVP signatures. The method described here, informational connectivity (IC), can identify regions with correlated changes in MVP-discriminability across time, revealing connectivity that is not accessible to FC. The method can be exploratory, using searchlights to identify seed-connected areas, or planned, between pre-selected regions-of-interest. The results can elucidate networks of regions that process MVP-related conditions, can breakdown MVPA searchlight maps into separate networks, or can be compared across tasks and patient groups.

Electric Cell-substrate Impedance Sensing (ECIS) is an in vitro impedance measuring system to quantify the behavior of cells within adherent cell layers. To this end, cells are grown in special culture chambers on top of opposing, circular gold electrodes. A constant small alternating current is applied between the electrodes and the potential across is measured. The insulating properties of the cell membrane create a resistance towards the electrical current flow resulting in an increased electrical potential between the electrodes. Measuring cellular impedance in this manner allows the automated study of cell attachment, growth, morphology, function, and motility. Although the ECIS measurement itself is straightforward and easy to learn, the underlying theory is complex and selection of the right settings and correct analysis and interpretation of the data is not self-evident. Yet, a clear protocol describing the individual steps from the experimental design to preparation, realization, and analysis of the experiment is not available. In this article the basic measurement principle as well as possible applications, experimental considerations, advantages and limitations of the ECIS system are discussed. A guide is provided for the study of cell attachment, spreading and proliferation; quantification of cell behavior in a confluent layer, with regard to barrier function, cell motility, quality of cell-cell and cell-substrate adhesions; and quantification of wound healing and cellular responses to vasoactive stimuli. Representative results are discussed based on human microvascular (MVEC) and human umbilical vein endothelial cells (HUVEC), but are applicable to all adherent growing cells.

A Protocol for Computer-Based Protein Structure and Function Prediction

Authors: Ambrish Roy, Dong Xu, Jonathan Poisson, Yang Zhang.

Institutions: University of Michigan , University of Kansas.

Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the understanding of their biological role. Although experimental methods can provide detailed information for a small fraction of these proteins, computational modeling is needed for the majority of protein molecules which are experimentally uncharacterized. The I-TASSER server is an on-line workbench for high-resolution modeling of protein structure and function. Given a protein sequence, a typical output from the I-TASSER server includes secondary structure prediction, predicted solvent accessibility of each residue, homologous template proteins detected by threading and structure alignments, up to five full-length tertiary structural models, and structure-based functional annotations for enzyme classification, Gene Ontology terms and protein-ligand binding sites. All the predictions are tagged with a confidence score which tells how accurate the predictions are without knowing the experimental data. To facilitate the special requests of end users, the server provides channels to accept user-specified inter-residue distance and contact maps to interactively change the I-TASSER modeling; it also allows users to specify any proteins as template, or to exclude any template proteins during the structure assembly simulations. The structural information could be collected by the users based on experimental evidences or biological insights with the purpose of improving the quality of I-TASSER predictions. The server was evaluated as the best programs for protein structure and function predictions in the recent community-wide CASP experiments. There are currently >20,000 registered scientists from over 100 countries who are using the on-line I-TASSER server.

Institutions: National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ).

Linear-amplification mediated PCR (LAM-PCR) has been developed to study hematopoiesis in gene corrected cells of patients treated by gene therapy with integrating vector systems. Due to the stable integration of retroviral vectors, integration sites can be used to study the clonal fate of individual cells and their progeny. LAM- PCR for the first time provided evidence that leukemia in gene therapy treated patients originated from provirus induced overexpression of a neighboring proto-oncogene. The high sensitivity and specificity of LAM-PCR compared to existing methods like inverse PCR and ligation mediated (LM)-PCR is achieved by an initial preamplification step (linear PCR of 100 cycles) using biotinylated vector specific primers which allow subsequent reaction steps to be carried out on solid phase (magnetic beads). LAM-PCR is currently the most sensitive method available to identify unknown DNA which is located in the proximity of known DNA. Recently, a variant of LAM-PCR has been developed that circumvents restriction digest thus abrogating retrieval bias of integration sites and enables a comprehensive analysis of provirus locations in host genomes. The following protocol explains step-by-step the amplification of both 3’- and 5’- sequences adjacent to the integrated lentiviral vector.

Institutions: University of North Carolina School of Medicine, University of North Carolina School of Medicine, University of North Carolina School of Medicine, University of North Carolina School of Medicine, University of North Carolina School of Medicine, Emory University School of Medicine, University of North Carolina School of Medicine.

Current astrocytoma models are limited in their ability to define the roles of oncogenic mutations in specific brain cell types during disease pathogenesis and their utility for preclinical drug development. In order to design a better model system for these applications, phenotypically wild-type cortical astrocytes and neural stem cells (NSC) from conditional, genetically engineered mice (GEM) that harbor various combinations of floxed oncogenic alleles were harvested and grown in culture. Genetic recombination was induced in vitro using adenoviral Cre-mediated recombination, resulting in expression of mutated oncogenes and deletion of tumor suppressor genes. The phenotypic consequences of these mutations were defined by measuring proliferation, transformation, and drug response in vitro. Orthotopic allograft models, whereby transformed cells are stereotactically injected into the brains of immune-competent, syngeneic littermates, were developed to define the role of oncogenic mutations and cell type on tumorigenesis in vivo. Unlike most established human glioblastoma cell line xenografts, injection of transformed GEM-derived cortical astrocytes into the brains of immune-competent littermates produced astrocytomas, including the most aggressive subtype, glioblastoma, that recapitulated the histopathological hallmarks of human astrocytomas, including diffuse invasion of normal brain parenchyma. Bioluminescence imaging of orthotopic allografts from transformed astrocytes engineered to express luciferase was utilized to monitor in vivo tumor growth over time. Thus, astrocytoma models using astrocytes and NSC harvested from GEM with conditional oncogenic alleles provide an integrated system to study the genetics and cell biology of astrocytoma pathogenesis in vitro and in vivo and may be useful in preclinical drug development for these devastating diseases.

The blood brain barrier (BBB) specifically regulates molecular and cellular flux between the blood and the nervous tissue. Our aim was to develop and characterize a highly reproducible rat syngeneic in vitro model of the BBB using co-cultures of primary rat brain endothelial cells (RBEC) and astrocytes to study receptors involved in transcytosis across the endothelial cell monolayer. Astrocytes were isolated by mechanical dissection following trypsin digestion and were frozen for later co-culture. RBEC were isolated from 5-week-old rat cortices. The brains were cleaned of meninges and white matter, and mechanically dissociated following enzymatic digestion. Thereafter, the tissue homogenate was centrifuged in bovine serum albumin to separate vessel fragments from nervous tissue. The vessel fragments underwent a second enzymatic digestion to free endothelial cells from their extracellular matrix. The remaining contaminating cells such as pericytes were further eliminated by plating the microvessel fragments in puromycin-containing medium. They were then passaged onto filters for co-culture with astrocytes grown on the bottom of the wells. RBEC expressed high levels of tight junction (TJ) proteins such as occludin, claudin-5 and ZO-1 with a typical localization at the cell borders. The transendothelial electrical resistance (TEER) of brain endothelial monolayers, indicating the tightness of TJs reached 300 ohm·cm2 on average. The endothelial permeability coefficients (Pe) for lucifer yellow (LY) was highly reproducible with an average of 0.26 ± 0.11 x 10-3 cm/min. Brain endothelial cells organized in monolayers expressed the efflux transporter P-glycoprotein (P-gp), showed a polarized transport of rhodamine 123, a ligand for P-gp, and showed specific transport of transferrin-Cy3 and DiILDL across the endothelial cell monolayer. In conclusion, we provide a protocol for setting up an in vitro BBB model that is highly reproducible due to the quality assurance methods, and that is suitable for research on BBB transporters and receptors.

Deriving the Time Course of Glutamate Clearance with a Deconvolution Analysis of Astrocytic Transporter Currents

Authors: Annalisa Scimemi, Jeffrey S. Diamond.

Institutions: National Institutes of Health.

The highest density of glutamate transporters in the brain is found in astrocytes. Glutamate transporters couple the movement of glutamate across the membrane with the co-transport of 3 Na+ and 1 H+ and the counter-transport of 1 K+. The stoichiometric current generated by the transport process can be monitored with whole-cell patch-clamp recordings from astrocytes. The time course of the recorded current is shaped by the time course of the glutamate concentration profile to which astrocytes are exposed, the kinetics of glutamate transporters, and the passive electrotonic properties of astrocytic membranes. Here we describe the experimental and analytical methods that can be used to record glutamate transporter currents in astrocytes and isolate the time course of glutamate clearance from all other factors that shape the waveform of astrocytic transporter currents. The methods described here can be used to estimate the lifetime of flash-uncaged and synaptically-released glutamate at astrocytic membranes in any region of the central nervous system during health and disease.

Inhibitory neurons act in the central nervous system to regulate the dynamics and spatio-temporal co-ordination of neuronal networks. GABA (γ-aminobutyric acid) is the predominant inhibitory neurotransmitter in the brain. It is released from the presynaptic terminals of inhibitory neurons within highly specialized intercellular junctions known as synapses, where it binds to GABAA receptors (GABAARs) present at the plasma membrane of the synapse-receiving, postsynaptic neurons. Activation of these GABA-gated ion channels leads to influx of chloride resulting in postsynaptic potential changes that decrease the probability that these neurons will generate action potentials.
During development, diverse types of inhibitory neurons with distinct morphological, electrophysiological and neurochemical characteristics have the ability to recognize their target neurons and form synapses which incorporate specific GABAARs subtypes. This principle of selective innervation of neuronal targets raises the question as to how the appropriate synaptic partners identify each other.
To elucidate the underlying molecular mechanisms, a novel in vitro co-culture model system was established, in which medium spiny GABAergic neurons, a highly homogenous population of neurons isolated from the embryonic striatum, were cultured with stably transfected HEK293 cell lines that express different GABAAR subtypes. Synapses form rapidly, efficiently and selectively in this system, and are easily accessible for quantification. Our results indicate that various GABAAR subtypes differ in their ability to promote synapse formation, suggesting that this reduced in vitro model system can be used to reproduce, at least in part, the in vivo conditions required for the recognition of the appropriate synaptic partners and formation of specific synapses. Here the protocols for culturing the medium spiny neurons and generating HEK293 cells lines expressing GABAARs are first described, followed by detailed instructions on how to combine these two cell types in co-culture and analyze the formation of synaptic contacts.

Institutions: Heart Research Center Goettingen, University Medical Center Goettingen, German Center for Cardiovascular Research (DZHK) partner site Goettingen, University of Maryland School of Medicine.

In cardiac myocytes a complex network of membrane tubules - the transverse-axial tubule system (TATS) - controls deep intracellular signaling functions. While the outer surface membrane and associated TATS membrane components appear to be continuous, there are substantial differences in lipid and protein content. In ventricular myocytes (VMs), certain TATS components are highly abundant contributing to rectilinear tubule networks and regular branching 3D architectures. It is thought that peripheral TATS components propagate action potentials from the cell surface to thousands of remote intracellular sarcoendoplasmic reticulum (SER) membrane contact domains, thereby activating intracellular Ca2+ release units (CRUs). In contrast to VMs, the organization and functional role of TATS membranes in atrial myocytes (AMs) is significantly different and much less understood. Taken together, quantitative structural characterization of TATS membrane networks in healthy and diseased myocytes is an essential prerequisite towards better understanding of functional plasticity and pathophysiological reorganization. Here, we present a strategic combination of protocols for direct quantitative analysis of TATS membrane networks in living VMs and AMs. For this, we accompany primary cell isolations of mouse VMs and/or AMs with critical quality control steps and direct membrane staining protocols for fluorescence imaging of TATS membranes. Using an optimized workflow for confocal or superresolution TATS image processing, binarized and skeletonized data are generated for quantitative analysis of the TATS network and its components. Unlike previously published indirect regional aggregate image analysis strategies, our protocols enable direct characterization of specific components and derive complex physiological properties of TATS membrane networks in living myocytes with high throughput and open access software tools. In summary, the combined protocol strategy can be readily applied for quantitative TATS network studies during physiological myocyte adaptation or disease changes, comparison of different cardiac or skeletal muscle cell types, phenotyping of transgenic models, and pharmacological or therapeutic interventions.

Complementary structural and functional neuroimaging techniques used to examine the Default Mode Network (DMN) could potentially improve assessments of psychiatric illness severity and provide added validity to the clinical diagnostic process. Recent neuroimaging research suggests that DMN processes may be disrupted in a number of stress-related psychiatric illnesses, such as posttraumatic stress disorder (PTSD).
Although specific DMN functions remain under investigation, it is generally thought to be involved in introspection and self-processing. In healthy individuals it exhibits greatest activity during periods of rest, with less activity, observed as deactivation, during cognitive tasks, e.g., working memory. This network consists of the medial prefrontal cortex, posterior cingulate cortex/precuneus, lateral parietal cortices and medial temporal regions.
Multiple functional and structural imaging approaches have been developed to study the DMN. These have unprecedented potential to further the understanding of the function and dysfunction of this network. Functional approaches, such as the evaluation of resting state connectivity and task-induced deactivation, have excellent potential to identify targeted neurocognitive and neuroaffective (functional) diagnostic markers and may indicate illness severity and prognosis with increased accuracy or specificity. Structural approaches, such as evaluation of morphometry and connectivity, may provide unique markers of etiology and long-term outcomes. Combined, functional and structural methods provide strong multimodal, complementary and synergistic approaches to develop valid DMN-based imaging phenotypes in stress-related psychiatric conditions. This protocol aims to integrate these methods to investigate DMN structure and function in PTSD, relating findings to illness severity and relevant clinical factors.

When considering human neuroimaging data, an appreciation of signal variability represents a fundamental innovation in the way we think about brain signal. Typically, researchers represent the brain's response as the mean across repeated experimental trials and disregard signal fluctuations over time as "noise". However, it is becoming clear that brain signal variability conveys meaningful functional information about neural network dynamics. This article describes the novel method of multiscale entropy (MSE) for quantifying brain signal variability. MSE may be particularly informative of neural network dynamics because it shows timescale dependence and sensitivity to linear and nonlinear dynamics in the data.

Institutions: University of Toronto, University of Toronto, University of Regina.

Phenotypes are determined by a complex series of physical (e.g. protein-protein) and functional (e.g. gene-gene or genetic) interactions (GI)1. While physical interactions can indicate which bacterial proteins are associated as complexes, they do not necessarily reveal pathway-level functional relationships1. GI screens, in which the growth of double mutants bearing two deleted or inactivated genes is measured and compared to the corresponding single mutants, can illuminate epistatic dependencies between loci and hence provide a means to query and discover novel functional relationships2. Large-scale GI maps have been reported for eukaryotic organisms like yeast3-7, but GI information remains sparse for prokaryotes8, which hinders the functional annotation of bacterial genomes. To this end, we and others have developed high-throughput quantitative bacterial GI screening methods9, 10.
Here, we present the key steps required to perform quantitative E. coli Synthetic Genetic Array (eSGA) screening procedure on a genome-scale9, using natural bacterial conjugation and homologous recombination to systemically generate and measure the fitness of large numbers of double mutants in a colony array format. Briefly, a robot is used to transfer, through conjugation, chloramphenicol (Cm) - marked mutant alleles from engineered Hfr (High frequency of recombination) 'donor strains' into an ordered array of kanamycin (Kan) - marked F- recipient strains. Typically, we use loss-of-function single mutants bearing non-essential gene deletions (e.g. the 'Keio' collection11) and essential gene hypomorphic mutations (i.e. alleles conferring reduced protein expression, stability, or activity9, 12, 13) to query the functional associations of non-essential and essential genes, respectively. After conjugation and ensuing genetic exchange mediated by homologous recombination, the resulting double mutants are selected on solid medium containing both antibiotics. After outgrowth, the plates are digitally imaged and colony sizes are quantitatively scored using an in-house automated image processing system14. GIs are revealed when the growth rate of a double mutant is either significantly better or worse than expected9. Aggravating (or negative) GIs often result between loss-of-function mutations in pairs of genes from compensatory pathways that impinge on the same essential process2. Here, the loss of a single gene is buffered, such that either single mutant is viable. However, the loss of both pathways is deleterious and results in synthetic lethality or sickness (i.e. slow growth). Conversely, alleviating (or positive) interactions can occur between genes in the same pathway or protein complex2 as the deletion of either gene alone is often sufficient to perturb the normal function of the pathway or complex such that additional perturbations do not reduce activity, and hence growth, further. Overall, systematically identifying and analyzing GI networks can provide unbiased, global maps of the functional relationships between large numbers of genes, from which pathway-level information missed by other approaches can be inferred9.

Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo. The present applications are designed to investigate differences of WM involvement patterns in different brain diseases, especially neurodegenerative disorders, by use of different DTI analyses in comparison with matched controls.
DTI data analysis is performed in a variate fashion, i.e. voxelwise comparison of regional diffusion direction-based metrics such as fractional anisotropy (FA), together with fiber tracking (FT) accompanied by tractwise fractional anisotropy statistics (TFAS) at the group level in order to identify differences in FA along WM structures, aiming at the definition of regional patterns of WM alterations at the group level. Transformation into a stereotaxic standard space is a prerequisite for group studies and requires thorough data processing to preserve directional inter-dependencies. The present applications show optimized technical approaches for this preservation of quantitative and directional information during spatial normalization in data analyses at the group level. On this basis, FT techniques can be applied to group averaged data in order to quantify metrics information as defined by FT. Additionally, application of DTI methods, i.e. differences in FA-maps after stereotaxic alignment, in a longitudinal analysis at an individual subject basis reveal information about the progression of neurological disorders. Further quality improvement of DTI based results can be obtained during preprocessing by application of a controlled elimination of gradient directions with high noise levels.
In summary, DTI is used to define a distinct WM pathoanatomy of different brain diseases by the combination of whole brain-based and tract-based DTI analysis.

We describe a high-throughput, high-volume, fully automated, live-in 24/7 behavioral testing system for assessing the effects of genetic and pharmacological manipulations on basic mechanisms of cognition and learning in mice. A standard polypropylene mouse housing tub is connected through an acrylic tube to a standard commercial mouse test box. The test box has 3 hoppers, 2 of which are connected to pellet feeders. All are internally illuminable with an LED and monitored for head entries by infrared (IR) beams. Mice live in the environment, which eliminates handling during screening. They obtain their food during two or more daily feeding periods by performing in operant (instrumental) and Pavlovian (classical) protocols, for which we have written protocol-control software and quasi-real-time data analysis and graphing software. The data analysis and graphing routines are written in a MATLAB-based language created to simplify greatly the analysis of large time-stamped behavioral and physiological event records and to preserve a full data trail from raw data through all intermediate analyses to the published graphs and statistics within a single data structure. The data-analysis code harvests the data several times a day and subjects it to statistical and graphical analyses, which are automatically stored in the "cloud" and on in-lab computers. Thus, the progress of individual mice is visualized and quantified daily. The data-analysis code talks to the protocol-control code, permitting the automated advance from protocol to protocol of individual subjects. The behavioral protocols implemented are matching, autoshaping, timed hopper-switching, risk assessment in timed hopper-switching, impulsivity measurement, and the circadian anticipation of food availability. Open-source protocol-control and data-analysis code makes the addition of new protocols simple. Eight test environments fit in a 48 in x 24 in x 78 in cabinet; two such cabinets (16 environments) may be controlled by one computer.

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Authors: Johannes Felix Buyel, Rainer Fischer.

Institutions: RWTH Aachen University, Fraunhofer Gesellschaft.

Plants provide multiple benefits for the production of biopharmaceuticals including low costs, scalability, and safety. Transient expression offers the additional advantage of short development and production times, but expression levels can vary significantly between batches thus giving rise to regulatory concerns in the context of good manufacturing practice. We used a design of experiments (DoE) approach to determine the impact of major factors such as regulatory elements in the expression construct, plant growth and development parameters, and the incubation conditions during expression, on the variability of expression between batches. We tested plants expressing a model anti-HIV monoclonal antibody (2G12) and a fluorescent marker protein (DsRed). We discuss the rationale for selecting certain properties of the model and identify its potential limitations. The general approach can easily be transferred to other problems because the principles of the model are broadly applicable: knowledge-based parameter selection, complexity reduction by splitting the initial problem into smaller modules, software-guided setup of optimal experiment combinations and step-wise design augmentation. Therefore, the methodology is not only useful for characterizing protein expression in plants but also for the investigation of other complex systems lacking a mechanistic description. The predictive equations describing the interconnectivity between parameters can be used to establish mechanistic models for other complex systems.

The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific properties, such as binding affinity, agonist or antagonist behavior, or stability, relative to the native sequence. Protein design lies at the center of current advances drug design and discovery. Not only does protein design provide predictions for potentially useful drug targets, but it also enhances our understanding of the protein folding process and protein-protein interactions. Experimental methods such as directed evolution have shown success in protein design. However, such methods are restricted by the limited sequence space that can be searched tractably. In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo protein design methods capable of tackling several important areas of protein design. These include the design of monomeric proteins for increased stability and complexes for increased binding affinity.
To disseminate these methods for broader use we present Protein WISDOM (http://www.proteinwisdom.org), a tool that provides automated methods for a variety of protein design problems. Structural templates are submitted to initialize the design process. The first stage of design is an optimization sequence selection stage that aims at improving stability through minimization of potential energy in the sequence space. Selected sequences are then run through a fold specificity stage and a binding affinity stage. A rank-ordered list of the sequences for each step of the process, along with relevant designed structures, provides the user with a comprehensive quantitative assessment of the design. Here we provide the details of each design method, as well as several notable experimental successes attained through the use of the methods.

Modern 3D electron microscopy approaches have recently allowed unprecedented insight into the 3D ultrastructural organization of cells and tissues, enabling the visualization of large macromolecular machines, such as adhesion complexes, as well as higher-order structures, such as the cytoskeleton and cellular organelles in their respective cell and tissue context. Given the inherent complexity of cellular volumes, it is essential to first extract the features of interest in order to allow visualization, quantification, and therefore comprehension of their 3D organization. Each data set is defined by distinct characteristics, e.g., signal-to-noise ratio, crispness (sharpness) of the data, heterogeneity of its features, crowdedness of features, presence or absence of characteristic shapes that allow for easy identification, and the percentage of the entire volume that a specific region of interest occupies. All these characteristics need to be considered when deciding on which approach to take for segmentation.
The six different 3D ultrastructural data sets presented were obtained by three different imaging approaches: resin embedded stained electron tomography, focused ion beam- and serial block face- scanning electron microscopy (FIB-SEM, SBF-SEM) of mildly stained and heavily stained samples, respectively. For these data sets, four different segmentation approaches have been applied: (1) fully manual model building followed solely by visualization of the model, (2) manual tracing segmentation of the data followed by surface rendering, (3) semi-automated approaches followed by surface rendering, or (4) automated custom-designed segmentation algorithms followed by surface rendering and quantitative analysis. Depending on the combination of data set characteristics, it was found that typically one of these four categorical approaches outperforms the others, but depending on the exact sequence of criteria, more than one approach may be successful. Based on these data, we propose a triage scheme that categorizes both objective data set characteristics and subjective personal criteria for the analysis of the different data sets.

We present a method to use the commercially available LEGO Mindstorms NXT robotics platform to test systems level neuroscience hypotheses. The first step of the method is to develop a nervous system simulation of specific reflexive behaviors of an appropriate model organism; here we use the American Lobster. Exteroceptive reflexes mediated by decussating (crossing) neural connections can explain an animal's taxis towards or away from a stimulus as described by Braitenberg and are particularly well suited for investigation using the NXT platform.1 The nervous system simulation is programmed using LabVIEW software on the LEGO Mindstorms platform. Once the nervous system is tuned properly, behavioral experiments are run on the robot and on the animal under identical environmental conditions. By controlling the sensory milieu experienced by the specimens, differences in behavioral outputs can be observed. These differences may point to specific deficiencies in the nervous system model and serve to inform the iteration of the model for the particular behavior under study. This method allows for the experimental manipulation of electronic nervous systems and serves as a way to explore neuroscience hypotheses specifically regarding the neurophysiological basis of simple innate reflexive behaviors. The LEGO Mindstorms NXT kit provides an affordable and efficient platform on which to test preliminary biomimetic robot control schemes. The approach is also well suited for the high school classroom to serve as the foundation for a hands-on inquiry-based biorobotics curriculum.

Cultured mammalian cells are used extensively in cell biology studies. It requires a number of special skills in order to be able to preserve the structure, function, behavior, and biology of the cells in culture. This video describes the basic skills required to freeze and store cells and how to recover frozen stocks.

Institutions: University of Alberta, University of Illinois, University of Alberta, University of Alberta, University of Alberta, University of Illinois at Urbana-Champaign, University of Illinois at Urbana-Champaign.

The ability to gauge social interactions is crucial in the assessment of others’ intentions. Factors such as facial expressions and body language affect our decisions in personal and professional life alike 1. These "friend or foe" judgements are often based on first impressions, which in turn may affect our decisions to "approach or avoid". Previous studies investigating the neural correlates of social cognition tended to use static facial stimuli 2. Here, we illustrate an experimental design in which whole-body animated characters were used in conjunction with functional magnetic resonance imaging (fMRI) recordings. Fifteen participants were presented with short movie-clips of guest-host interactions in a business setting, while fMRI data were recorded; at the end of each movie, participants also provided ratings of the host behaviour. This design mimics more closely real-life situations, and hence may contribute to better understanding of the neural mechanisms of social interactions in healthy behaviour, and to gaining insight into possible causes of deficits in social behaviour in such clinical conditions as social anxiety and autism 3.

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

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We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.